Identical fracture patterns in combat vehicle blast injuries due to improvised explosive devices; a case series
Bibliographic record
Abstract
BACKGROUND: In November 2008, a surgical team from the Red Cross Hospital Beverwijk, the Netherlands, was deployed in Afghanistan for three months to attend in the army hospital of Kandahar.During their stay, four incidents of armored personnel carriers encountering an improvised explosive device were assessed. In each incident, two soldiers were involved, whose injuries were strikingly similar. CASE PRESENTATION: The described cases comprise paired thoracic vertebral fractures, radial neck fractures, calcaneal fractures and talar fractures. Moreover, the different types of blast injury are mentioned and related to the injuries described in our series. Acknowledging the different blast mechanisms is important for understanding possible injury patterns. CONCLUSION: From this case series, as well as the existing literature on injury patterns caused by blast injuries, it seems appropriate to pay extra attention to bodily areas that were injured in other occupants of the same vehicle. Obviously, the additional surveillance for specific injuries should be complementary to the regular trauma work-up (e.g., ATLS).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".